Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demographic groups to be treated fairly. However, algorithms that aim to satisfy inter-group fairness (also called group fairness) may inadvertently treat individuals within the same demographic group unfairly. To address this issue, we introduce a formal definition of within-group fairness that maintains fairness among individuals from within the same group. We propose a pre-processing framework to meet both inter- and within-group fairness criteria with little compromise in accuracy. The framework maps the feature vectors of members from different groups to an inter-group-fair canonical domain before feeding them into a scoring function. The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness. We apply this framework to the COMPAS risk assessment and Law School datasets and compare its performance in achieving inter-group and within-group fairness to two regularization-based methods.

翻译：随着机器学习算法在涉及敏感数据的应用场景中部署日益增多，算法公平性问题引起了广泛关注。现有研究主要聚焦于需要公平对待不同人口群体的应用场景。然而，旨在满足群体间公平性（亦称群组公平性）的算法可能无意间对同一个人口群体内的个体造成不公。针对这一问题，我们提出了群体内公平性的正式定义，以维护同群体内个体之间的公平性。我们设计了一种预处理框架，在几乎不影响准确率的前提下同时满足群体间公平性与群体内公平性标准。该框架将不同群体的特征向量映射至满足群体间公平性的规范域，再将其输入评分函数。通过构建保留同群体个体原始特征向量所获评分相对关系的映射关系，确保群体内公平性。我们将该框架应用于COMPAS风险评估与法学院数据集，并对比了其与两种基于正则化方法在实现群体间与群体内公平性方面的性能表现。